Skill

context-optimizer

Analyzes Claude Code skill content for optimal placement (Skill vs Passive Context vs Hybrid), compresses markdown to pipe-delimited format (60-80% token reduction), and validates compliance with decision framework.

Python

developer-tools

Install

npx claudepluginhub rjmurillo/ai-agents --plugin project-toolkit

Tool Access

This skill is limited to using the following tools:

viewbashglob

Preview

Tooling suite for optimizing Claude Code context placement. Passive context (AGENTS.md, @imports) achieves 100% pass rates versus 53-79% for skills by eliminating decision points.

SKILL.md

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Parent Repo Stars15

Parent Repo Forks4

Last CommitMar 9, 2026

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Context Optimizer

Tooling suite for optimizing Claude Code context placement. Passive context (AGENTS.md, @imports) achieves 100% pass rates versus 53-79% for skills by eliminating decision points.

Triggers

analyze skill placement - classify content as Skill vs Passive Context
compress markdown - reduce token count for context files
validate compliance - check skill/passive context placement decisions
optimize context - lower API costs and improve agent performance
extract and index - split markdown into detail files with compact index

Process

Analyze: Run analyze_skill_placement.py to classify content
Compress: Run compress_markdown_content.py to reduce token counts
Validate: Run test_skill_passive_compliance.py to check compliance
Verify: Confirm output JSON contains expected classification and metrics

Verification

Classification matches expected type (Skill/PassiveContext/Hybrid)
Compression achieves target reduction (40-80% depending on level)
Compliance validator returns exit code 0
Output JSON is valid and contains all required fields

Scripts

Script	Purpose	Exit Codes
`analyze_skill_placement.py`	Classify content as Skill/PassiveContext/Hybrid	0=success, 1=error
`compress_markdown_content.py`	Compress markdown with token reduction metrics	0=success, 1=error, 2=config, 3=external
`test_skill_passive_compliance.py`	Validate compliance with decision framework	0=pass, 1=violations
`extract_and_index.py`	Extract sections into detail files with pipe-delimited index	0=success, 1=error, 2=config, 3=external
`path_validation.py`	Shared CWE-22 repo-root-anchored path validation	N/A (library module)

Prerequisites

Python 3.12+ with tiktoken for local token counting:

uv pip install -e ".[dev]"   # includes tiktoken
pip install tiktoken           # or install directly

tiktoken is an offline tokenizer (cl100k_base encoding) that approximates Claude tokenization. No API key is required for these scripts.

Decision Framework

Use Passive Context For

Framework knowledge (APIs, patterns, conventions)
Always-needed information (constraints, protocols, gates)
Domain concepts (terminology, relationships)
Routing rules (comment classification, agent selection)
Reference data (memory indexes, skill catalogs)

Use Skills For

Tool-based actions (file modification, API calls, git operations)
User-triggered workflows (PR creation, issue management)
Multi-step procedures (conflict resolution, session completion)
Actions requiring validation (security scans, linting)
Versioned, team-reviewed instructions across projects

Hybrid Pattern

Knowledge in passive context (routing, classification)
Actions in skill (script execution, state changes)
Example: pr-comment-responder has routing in SKILL-QUICK-REF.md, scripts in skill

Why This Matters

Configuration	Pass Rate
Baseline (no docs)	53%
Skill (default)	53%
Skill + explicit instructions	79%
AGENTS.md passive context	100%

Skills create decision points where agents must choose whether to retrieve documentation. These introduce 4 failure modes: late retrieval, partial retrieval, integration failure, and instruction fragility. Passive context eliminates all four by being always-available.

References

Vercel: AGENTS.md outperforms skills
Analysis: .agents/analysis/vercel-passive-context-vs-skills-research.md
Memory: passive-context-vs-skills-vercel-research
Decision framework: SKILL-QUICK-REF.md (see the "Decision Framework" section)

Issue #1108: Build passive context tooling suite

Tool Details: Skill/Passive Content Analyzer

Script: scripts/analyze_skill_placement.py

Analyzes skill content and recommends Skill, Passive Context, or Hybrid placement.

Classification Logic:

Tool Calls: Bash, Read, Write, Edit, gh, git, pwsh commands -> Skill
Action Verbs: create, update, delete, execute, run -> Skill
Reference Content: Tables, lists, code blocks -> Passive
User Triggers: "when user", slash commands, explicit requests -> Skill
Always-Needed: "always", "mandatory", "framework knowledge" -> Passive

Usage:

# Analyze a skill directory (from repo root)
python3 .claude/skills/context-optimizer/scripts/analyze_skill_placement.py -p .claude/skills/github

# Analyze a specific SKILL.md
python3 .claude/skills/context-optimizer/scripts/analyze_skill_placement.py -p .claude/skills/github/SKILL.md

# Get detailed metrics
python3 .claude/skills/context-optimizer/scripts/analyze_skill_placement.py -p .claude/skills/github -d

Output:

{
  "classification": "Hybrid",
  "confidence": 85,
  "reasoning": "High tool execution (12 calls); High reference content ratio (0.75)",
  "recommendations": {
    "Passive": ["Routing Rules", "Classification Framework"],
    "Skill": ["Get-UnaddressedComments.ps1", "Post-PRCommentReply.ps1"]
  }
}

Classification Thresholds:

Classification	Criteria	Confidence
Skill	skillScore > passiveScore + 3	70-90%
PassiveContext	passiveScore > skillScore + 3	70-90%
Hybrid	abs(skillScore - passiveScore) <= 3	50-70%

Tool Details: Content Compression Utility

Script: scripts/compress_markdown_content.py

Compress markdown to pipe-delimited format achieving 60-80% token reduction while maintaining 100% information density.

Compression Techniques:

Convert tables to pipe-delimited: |key: value|key2: value2|
Extract headings to index: [Section] |item1 |item2
Strip redundant words (the, a, an, is, are)
Collapse whitespace and abbreviate common terms
Preserve code blocks

Usage:

# Basic compression (JSON output to stdout)
python3 scripts/compress_markdown_content.py -i README.md -l medium

# Save to file with aggressive compression
python3 scripts/compress_markdown_content.py -i CRITICAL-CONTEXT.md -l aggressive -o compressed.txt

# With verbose metrics
python3 scripts/compress_markdown_content.py -i input.md -l medium -v

Compression Levels:

Level	Reduction	Techniques
Light	40-50%	Headers, tables, whitespace
Medium	50-60%	+ redundant words, tighter whitespace
Aggressive	60-80%	+ H3 compression, lists, abbreviations

Example (26 tokens -> 18 tokens, 31% reduction):

Before:

## Session Protocol

The session protocol has multiple phases:

1. Serena Activation - You must activate Serena

After:

[Session Protocol]
session protocol has multiple phases:
1. Serena Activation - activate Serena

Tool Details: Extract-and-Index Utility

Script: scripts/extract_and_index.py

Implements the Vercel extract-and-index pattern for 60-80% token reduction. Splits markdown by headings into detail files, generates a compact pipe-delimited index.

Usage:

# Extract sections and output JSON to stdout
python3 scripts/extract_and_index.py -i AGENTS.md -d .agents-details

# Write index to a file
python3 scripts/extract_and_index.py -i AGENTS.md -d .agents-details -o AGENTS-INDEX.md

# Custom reference path in index
python3 scripts/extract_and_index.py -i AGENTS.md -d .agents-details -r .agents-docs -o AGENTS-INDEX.md

Output Index Format (Vercel pattern):

[Architecture]
|Layered design with separation of concerns (see: .agents-details/architecture.md)
[Testing]
|80% coverage required for business logic (see: .agents-details/testing.md)

Works with CLAUDE.md @import mechanism. Reference via @AGENTS-INDEX.md.

Tool Details: Compliance Validator

Script: scripts/test_skill_passive_compliance.py

Validates content placement against the skill vs passive context decision framework.

6 Compliance Checks:

Skills contain actions (verbs, tool execution, scripts)
Passive context is knowledge-only (no action patterns)
CLAUDE.md under 200 lines (Anthropic recommendation)
@imported files exist and are readable
Skills have frontmatter (name and description)
No duplicate content between skills and passive context

Usage:

# Scan .claude directory (JSON output)
python3 scripts/test_skill_passive_compliance.py

# Scan specific directory with table output
python3 scripts/test_skill_passive_compliance.py --path .claude/skills/github --format table

Exit Codes: 0 = all passed, 1 = violations detected

Common Violations:

Violation	Fix
CLAUDE.md too long	Split into separate files, add @imports
Missing @import file	Create file or remove @import directive
Skill missing frontmatter	Add `---` block with `name:` and `description:`
Skill has no actions	Add scripts or move to passive context
Passive has actions	Extract executable content to a skill
Duplicate content	Remove redundant content from skill or passive

Classification Examples

Clear Skill Classification

Input: GitHub skill with gh pr create, gh issue close commands

{"classification": "Skill", "confidence": 85, "reasoning": "High tool execution (8 calls); Many action verbs (12)"}


### Clear Passive Classification

**Input**: Memory hierarchy reference with tables and always-needed patterns

```json

{"classification": "PassiveContext", "confidence": 90, "reasoning": "High reference content ratio (0.85); Always-needed information (5 indicators)"}

Hybrid Classification

Input: PR comment responder with routing rules + script execution

{
  "classification": "Hybrid",
  "confidence": 65,
  "reasoning": "High reference content ratio (0.72); Some tool execution (4 calls)",
  "recommendations": {
    "Passive": ["Routing Rules", "Classification Framework"],
    "Skill": ["Get-UnaddressedComments.ps1", "Post-PRCommentReply.ps1"]
  }
}

Testing

python3 -m pytest tests/                                          # all tests
python3 -m pytest tests/test_skill_passive_compliance_test.py -v  # specific
python3 -m pytest tests/ --cov=scripts --cov-report=term-missing  # coverage

Coverage Summary:

Component	Tests	Key Areas
Compliance Validator	19/20 (95%)	Line count, @imports, frontmatter, duplicates, exit codes
Analyzer	Full	Tool calls, action verbs, classification logic, confidence scoring
Extract-and-Index	36	Slug generation, parsing, index format, 60%+ reduction targets
Compressor	Full	All levels, code block preservation, 40-80% reduction targets

Implementation Notes

Language: Python 3.12+ per ADR-042 (PowerShell deprecated)
Testing: pytest with comprehensive coverage
Exit codes: ADR-035 standardization (0 = success, non-zero = failure)
Type safety: Full type hints using dataclasses and typing module
Cross-platform: pathlib for platform-independent path handling

Marketplace Value

Automated optimization: Compress context without manual editing
Quality gates: Enforce best practices in CI/CD
Token savings: 60-80% reduction = lower API costs

context-optimizer

Install

Tool Access

Preview

SKILL.md

Similar Skills

context-optimizer

Install

Tool Access

Preview

SKILL.md

Context Optimizer

Triggers

Process

Verification

Scripts

Prerequisites

Decision Framework

Use Passive Context For

Use Skills For

Hybrid Pattern

Why This Matters

References

Related

Clear Skill Classification

Hybrid Classification

Marketplace Value

Similar Skills

Context Optimizer

Triggers

Process

Verification

Scripts

Prerequisites

Decision Framework

Use Passive Context For

Use Skills For

Hybrid Pattern

Why This Matters

References

Related

Clear Skill Classification

Hybrid Classification

Marketplace Value